Text Generation
PEFT
Safetensors
Transformers
English
lora
simpo
preference-optimization
judge
evaluation
benchmark
sales
B2B
trl
unsloth
conversational
Instructions to use Birkity/tenacious-judge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Birkity/tenacious-judge with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-3B-Instruct") model = PeftModel.from_pretrained(base_model, "Birkity/tenacious-judge") - Transformers
How to use Birkity/tenacious-judge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Birkity/tenacious-judge") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Birkity/tenacious-judge", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Birkity/tenacious-judge with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Birkity/tenacious-judge" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Birkity/tenacious-judge", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Birkity/tenacious-judge
- SGLang
How to use Birkity/tenacious-judge with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Birkity/tenacious-judge" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Birkity/tenacious-judge", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Birkity/tenacious-judge" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Birkity/tenacious-judge", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Desktop
- Docker Model Runner
How to use Birkity/tenacious-judge with Docker Model Runner:
docker model run hf.co/Birkity/tenacious-judge
Download tokenizer.json from Birkity/tenacious-judge: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Birkity/tenacious-judge/resolve/main/tokenizer.json
- Command line
-
hf download hf://Birkity/tenacious-judge/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Birkity/tenacious-judge/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- fc23b0c3ff41a8a1324c8565bce595fe11422393117c438758cc6fcea9be9541
- Size of remote file:
- 11.4 MB
- SHA256:
- bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
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